DataArray provides a wrapper around numpy ndarrays that uses labeled
dimensions and coordinates to support metadata aware operations. The API is
similar to that for the pandas Series or DataFrame, but DataArray objects
can have any number of dimensions, and their contents have fixed data
types.

Values for this array. Must be an numpy.ndarray, ndarray like,
or castable to an ndarray. If a self-described xarray or pandas
object, attempts are made to use this array’s metadata to fill in
other unspecified arguments. A view of the array’s data is used
instead of a copy if possible.

coords:sequence or dict of array_like objects, optional

Coordinates (tick labels) to use for indexing along each dimension.
If dict-like, should be a mapping from dimension names to the
corresponding coordinates. If sequence-like, should be a sequence
of tuples where the first element is the dimension name and the
second element is the corresponding coordinate array_like object.

dims:str or sequence of str, optional

Name(s) of the data dimension(s). Must be either a string (only
for 1D data) or a sequence of strings with length equal to the
number of dimensions. If this argument is omitted, dimension names
are taken from coords (if possible) and otherwise default to
['dim_0',...'dim_n'].

name:str or None, optional

Name of this array.

attrs:dict_like or None, optional

Attributes to assign to the new instance. By default, an empty
attribute dictionary is initialized.

encoding:dict_like or None, optional

Dictionary specifying how to encode this array’s data into a
serialized format like netCDF4. Currently used keys (for netCDF)
include ‘_FillValue’, ‘scale_factor’, ‘add_offset’, ‘dtype’,
‘units’ and ‘calendar’ (the later two only for datetime arrays).
Unrecognized keys are ignored.